Multi-objective optimization on blade airfoil of vertical axis wind turbine

Vertical axis wind turbines (VAWTs) have garnered significant attention due to their omnidirectionality, low noise, suitability for turbulent conditions, and high efficiency in offshore cluster deployments. VAWTs are more sensitive to dynamic stall, which can lead to reduced performance and reliability. Selecting appropriate blade airfoils can enhance both performance and reliability. This study employs a parametric method to design 12 input parameters for controlling the blade airfoil, with the average power coefficient of a single vertical wind turbine blade and the coefficient of variation of the main shaft as output parameters. The Non-dominated Sorting Genetic Algorithm II multi-objective genetic algorithm is used for blade airfoil optimization, selecting the two best-performing airfoils from the Pareto front. The average power coefficient of a single blade increased by 8.6% and 4.3%, respectively, while the coefficient of variation decreased by up to 6.7% and 5.3%. The analysis of the blades at different azimuth angles indicates that optimized blades can suppress flow separation at high angles of attack, enable faster wake recovery, and reduce energy loss.